library(haven)
Data <- read.csv("/media/empro/5C02C2EB02C2C8EA/Ubuntu/Fendler/ovaryAnalysis/processed.csv", header = TRUE)
#View(Data)
Labels <- Data[c(1)]
Data <- Data[-c(1)]
zbadamy macierz korelacji
corMatrix <- cor(Data, method = 'spearman')
#heatm <- heatmap(corMatrix)
heatm <- heatmap(corMatrix, Rowv = NA, Colv = NA)

Mediany
library(data.table)
data.table 1.10.4.3
The fastest way to learn (by data.table authors): https://www.datacamp.com/courses/data-analysis-the-data-table-way
Documentation: ?data.table, example(data.table) and browseVignettes("data.table")
Release notes, videos and slides: http://r-datatable.com
View(Labels)
Sprawdz dystrybucje
library(ggplot2)
for(colmn in Data)
{
hist(colmn)
}


























































































































































































































































































































































































































































































































































































toM <- subset(Data, Labels[c(1)] == 'can')
#View(toM)
#toM <- setDT(Data, key=Labels)['can']
policz mediane
names <- c()
meds <- c()
ps <- c()
for(i in 1:ncol(toM))
{
med = median(toM[, i])
#print(median(as.numeric(as.vector(toM[c(i)]))) )
name = colnames(toM)[i]
p = wilcox.test(Data[, i] ~ Labels[, 1])$p.value
#cat("Nazwa: ", name, " mediana: ", med, " p: ", p, "\n")
names <- c(names, name)
meds <- c(meds, med)
ps <- c(ps, p)
}
#meds <- log2(meds)
volData = data.frame(names, meds, ps)
#View(head(volData, n=10) )
volData <- volData[order(volData$ps),]
View(head(volData, n=10) )
Beniamini-Hochberg Procedure - NOPE FDR
bh = c()
for(i in 1:nrow(volData))
{
#akt = volData[i, 'ps'] * 0.05 / nrow((volData))
akt = p.adjust(volData$ps, method = 'fdr')
bh <- c(bh , akt)
}
#View(head(bh, n=10))
volData <- data.frame(volData, bh)
row names were found from a short variable and have been discarded
View(head(volData, n=10))
Rysujemy piękny volcanoPlot
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:data.table’:
between, first, last
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, union
library(ggplot2)
View(volData)
drawRes = mutate(volData, sig=ifelse(volData$bh < 0.05, 'FDR<0.05', 'Not Sig'))
volPlo = ggplot(drawRes, aes(meds, -log10(ps))) + geom_point(aes(col=sig)) + scale_color_manual(values = c('red', 'black'))
volPlo <- volPlo + geom_text(data=filter(drawRes, bh<0.05), aes(label=names))
volPlo <- volPlo + scale_x_continuous(limits = c(-2, 2))
volPlo

move labels
library(ggrepel)
volPlo <- volPlo + geom_text_repel(data=filter(drawRes, bh < 0.05), aes(label=names))
volPlo
Machine Learning
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